plop VS casual

Almost all of us, while working on a project, are copy/pasting from the previous one while creating similar structures. While thinking if there is a fast way to do this, I came across plop js.

In this article, I will talk about Plop, which is a little tool that saves you time and helps your team build new files with consistency, as the documentation suggests.

My tool of choice is Plop.js. Plop is a powerful "micro-generator framework" built to help maintain patterns as well as speed up your project build time. From the documenation:

Plop

When it comes to software development, a good developer experience (or DX) is a key aspect in helping the development process to become faster and more efficient. Having a good DX means that developers are focusing on the real deal while other, less important stuff, is being taken care of by automated tools. Such a tool is Plop which I really love for its simplicity and efficiency.

this allows you to import this Header component with import { Header } from 'path/Header' without Header/Header. Creating this structure manually is annoying, so one will benefit from some code generators like plop or hygen.

plop for create a consistent templates for pages, components, etc

Plop

Faker looks cool! We've been using Casual [0] for ages but Faker looks more featureful and more actively maintained.

0: https://github.com/boo1ean/casual

In order to create the mockserver we need 2 npm dependencies. json-server and casual so we run npm install json-server casual --save-dev in our project.

https://npm.im/casual (57k weekly downloads) https://github.com/boo1ean/casual (2.8k stars)

In the past, I have relied on Google Sheets and a combination of array formulas, vlookups, random value generators to create fake datasets. This approach worked for small, one off datasets. Any time I needed to generate more than 1000 records or needed to update a dataset, this approach proved to be tedious. I also explored Faker and Casual (both of which are fantastic tools) but found that the data they generate was too abstract and that the implementation wasn't ideal for large datasets. I wanted a tool that: